


# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

_snake_case	:						Any         =							2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
_snake_case	:						List[str]         =							{
    # fairseq:
    "wmt19-ru-en": {"length_penalty": 1.1},
    "wmt19-en-ru": {"length_penalty": 1.15},
    "wmt19-en-de": {"length_penalty": 1.0},
    "wmt19-de-en": {"length_penalty": 1.1},
    # allenai:
    "wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
    "wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
    "wmt16-en-de-12-1": {"length_penalty": 0.8},
    "wmt19-de-en-6-6-base": {"length_penalty": 0.6},
    "wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}

# this remaps the different models to their organization names
_snake_case	:						int         =							{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
			_snake_case	:						Optional[Any]         =							"facebook"
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
			_snake_case	:						Optional[Any]         =							"allenai"







def 							lowerCAmelCase_	(   __lowerCamelCase      ):
							# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
							# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
							__snake_case						:     List[Any]									=  dict((re.sub(R"@@$"				,      ""				,      __lowerCamelCase      ), v) if k.endswith("@@"      ) else (re.sub(R"$"				,      "</w>"				,      __lowerCamelCase      ), v) for k, v in d.items()      )
							__snake_case						:     Tuple									=  "<s> <pad> </s> <unk>".split()
							# restore the special tokens
							for k in keep_keys:
														del da[F'{k}</w>']
														__snake_case						:     Optional[int]									=  d[k]  # restore
							return da







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase      ):
							# prep
							assert os.path.exists(__lowerCamelCase      )
							os.makedirs(__lowerCamelCase				,      exist_ok=__lowerCamelCase      )
							print(F'Writing results to {pytorch_dump_folder_path}'      )

							# handle various types of models

							__snake_case						:     Any									=  basename(__lowerCamelCase      )
							__snake_case						:     Union[str, Any]									=  dirname(__lowerCamelCase      )

							__snake_case						:     int									=  fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
							__snake_case						:     Optional[Any]									=  cls.hub_models()
							__snake_case						:     Union[str, Any]									=  {"bpe": "fastbpe", "tokenizer": "moses"}
							__snake_case						:     Any									=  "."
							# note: since the model dump is old, fairseq has upgraded its model some
							# time later, and it does a whole lot of rewrites and splits on the saved
							# weights, therefore we can't use torch.load() directly on the model file.
							# see: upgrade_state_dict(state_dict) in fairseq_model.py
							print(F'using checkpoint {checkpoint_file}'      )
							__snake_case						:     Dict									=  hub_utils.from_pretrained(
							    __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      archive_map=__lowerCamelCase				,      **__lowerCamelCase      )

							__snake_case						:     List[Any]									=  vars(chkpt["args"]["model"]      )

							__snake_case						:     int									=  args["source_lang"]
							__snake_case						:     str									=  args["target_lang"]

							__snake_case						:     List[str]									=  dirname(__lowerCamelCase      )
							__snake_case						:     str									=  basename(__lowerCamelCase      )

							# dicts
							__snake_case						:     str									=  os.path.join(__lowerCamelCase				,      F'dict.{src_lang}.txt'      )
							__snake_case						:     List[str]									=  os.path.join(__lowerCamelCase				,      F'dict.{tgt_lang}.txt'      )

							__snake_case						:     List[str]									=  Dictionary.load(__lowerCamelCase      )
							__snake_case						:     Any									=  rewrite_dict_keys(src_dict.indices      )
							__snake_case						:     str									=  len(__lowerCamelCase      )
							__snake_case						:     List[str]									=  os.path.join(__lowerCamelCase				,      "vocab-src.json"      )
							print(F'Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records'      )
							with open(__lowerCamelCase				,      "w"				,      encoding="utf-8"      ) as f:
														f.write(json.dumps(__lowerCamelCase				,      ensure_ascii=__lowerCamelCase				,      indent=__lowerCamelCase      )      )

							# detect whether this is a do_lower_case situation, which can be derived by checking whether we
							# have at least one uppercase letter in the source vocab
							__snake_case						:     Tuple									=  True
							for k in src_vocab.keys():
														if not k.islower():
																					__snake_case						:     Tuple									=  False
																					break

							__snake_case						:     Dict									=  Dictionary.load(__lowerCamelCase      )
							__snake_case						:     List[Any]									=  rewrite_dict_keys(tgt_dict.indices      )
							__snake_case						:     Any									=  len(__lowerCamelCase      )
							__snake_case						:     List[str]									=  os.path.join(__lowerCamelCase				,      "vocab-tgt.json"      )
							print(F'Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records'      )
							with open(__lowerCamelCase				,      "w"				,      encoding="utf-8"      ) as f:
														f.write(json.dumps(__lowerCamelCase				,      ensure_ascii=__lowerCamelCase				,      indent=__lowerCamelCase      )      )

							# merges_file (bpecodes)
							__snake_case						:     Union[str, Any]									=  os.path.join(__lowerCamelCase				,      VOCAB_FILES_NAMES["merges_file"]      )
							for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
														__snake_case						:     Any									=  os.path.join(__lowerCamelCase				,      __lowerCamelCase      )
														if os.path.exists(__lowerCamelCase      ):
																					break
							with open(__lowerCamelCase				,      encoding="utf-8"      ) as fin:
														__snake_case						:     int									=  fin.read()
							__snake_case						:     Any									=  re.sub(R" \d+$"				,      ""				,      __lowerCamelCase				,      0				,      re.M      )  # remove frequency number
							print(F'Generating {merges_file}'      )
							with open(__lowerCamelCase				,      "w"				,      encoding="utf-8"      ) as fout:
														fout.write(__lowerCamelCase      )

							# model config
							__snake_case						:     str									=  os.path.join(__lowerCamelCase				,      "config.json"      )

							# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
							# may have to modify the tokenizer if a different type is used by a future model
							assert args["bpe"] == "fastbpe", F'need to extend tokenizer to support bpe={args["bpe"]}'
							assert args["tokenizer"] == "moses", F'need to extend tokenizer to support bpe={args["tokenizer"]}'

							__snake_case						:     List[Any]									=  {
							    "architectures": ["FSMTForConditionalGeneration"],
							    "model_type": "fsmt",
							    "activation_dropout": args["activation_dropout"],
							    "activation_function": "relu",
							    "attention_dropout": args["attention_dropout"],
							    "d_model": args["decoder_embed_dim"],
							    "dropout": args["dropout"],
							    "init_std": 0.0_2,
							    "max_position_embeddings": args["max_source_positions"],
							    "num_hidden_layers": args["encoder_layers"],
							    "src_vocab_size": src_vocab_size,
							    "tgt_vocab_size": tgt_vocab_size,
							    "langs": [src_lang, tgt_lang],
							    "encoder_attention_heads": args["encoder_attention_heads"],
							    "encoder_ffn_dim": args["encoder_ffn_embed_dim"],
							    "encoder_layerdrop": args["encoder_layerdrop"],
							    "encoder_layers": args["encoder_layers"],
							    "decoder_attention_heads": args["decoder_attention_heads"],
							    "decoder_ffn_dim": args["decoder_ffn_embed_dim"],
							    "decoder_layerdrop": args["decoder_layerdrop"],
							    "decoder_layers": args["decoder_layers"],
							    "bos_token_id": 0,
							    "pad_token_id": 1,
							    "eos_token_id": 2,
							    "is_encoder_decoder": True,
							    "scale_embedding": not args["no_scale_embedding"],
							    "tie_word_embeddings": args["share_all_embeddings"],
							}

							# good hparam defaults to start with
							__snake_case						:     Optional[Any]									=  5
							__snake_case						:     List[Any]									=  False
							if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
														__snake_case						:     List[str]									=  best_score_hparams[model_dir]["length_penalty"]
							else:
														__snake_case						:     Tuple									=  1.0

							print(F'Generating {fsmt_model_config_file}'      )
							with open(__lowerCamelCase				,      "w"				,      encoding="utf-8"      ) as f:
														f.write(json.dumps(__lowerCamelCase				,      ensure_ascii=__lowerCamelCase				,      indent=__lowerCamelCase      )      )

							# tokenizer config
							__snake_case						:     List[str]									=  os.path.join(__lowerCamelCase				,      __lowerCamelCase      )

							__snake_case						:     Tuple									=  {
							    "langs": [src_lang, tgt_lang],
							    "model_max_length": 1_0_2_4,
							    "do_lower_case": do_lower_case,
							}

							print(F'Generating {fsmt_tokenizer_config_file}'      )
							with open(__lowerCamelCase				,      "w"				,      encoding="utf-8"      ) as f:
														f.write(json.dumps(__lowerCamelCase				,      ensure_ascii=__lowerCamelCase				,      indent=__lowerCamelCase      )      )

							# model
							__snake_case						:     List[Any]									=  chkpt["models"][0]
							__snake_case						:     Tuple									=  model.state_dict()

							# rename keys to start with 'model.'
							__snake_case						:     Union[str, Any]									=  OrderedDict(("model." + k, v) for k, v in model_state_dict.items()      )

							# remove unneeded keys
							__snake_case						:     Any									=  [
							    "model.model",
							    "model.encoder.version",
							    "model.decoder.version",
							    "model.encoder_embed_tokens.weight",
							    "model.decoder_embed_tokens.weight",
							    "model.encoder.embed_positions._float_tensor",
							    "model.decoder.embed_positions._float_tensor",
							]
							for k in ignore_keys:
														model_state_dict.pop(__lowerCamelCase				,      __lowerCamelCase      )

							__snake_case						:     Union[str, Any]									=  FSMTConfig.from_pretrained(__lowerCamelCase      )
							__snake_case						:     Union[str, Any]									=  FSMTForConditionalGeneration(__lowerCamelCase      )

							# check that it loads ok
							model_new.load_state_dict(__lowerCamelCase				,      strict=__lowerCamelCase      )

							# save
							__snake_case						:     int									=  os.path.join(__lowerCamelCase				,      __lowerCamelCase      )
							print(F'Generating {pytorch_weights_dump_path}'      )
							torch.save(__lowerCamelCase				,      __lowerCamelCase      )

							print("Conversion is done!"      )
							print("\nLast step is to upload the files to s3"      )
							print(F'cd {data_root}'      )
							print(F'transformers-cli upload {model_dir}'      )


if __name__ == "__main__":
			_snake_case	:						Any         =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--fsmt_checkpoint_path",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
			        " bpecodes, etc."
			    ),
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			_snake_case	:						Any         =							parser.parse_args()
			convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import DetrImageProcessor

class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case=7     ,   __snake_case=3     ,   __snake_case=3_0     ,   __snake_case=4_0_0     ,   __snake_case=True     ,   __snake_case=None     ,   __snake_case=True     ,   __snake_case=1 / 2_5_5     ,   __snake_case=True     ,   __snake_case=[0.5, 0.5, 0.5]     ,   __snake_case=[0.5, 0.5, 0.5]     ,   __snake_case=True     ,   ):
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         snake_case											=							size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
         snake_case											=							parent
         snake_case											=							batch_size
         snake_case											=							num_channels
         snake_case											=							min_resolution
         snake_case											=							max_resolution
         snake_case											=							do_resize
         snake_case											=							size
         snake_case											=							do_rescale
         snake_case											=							rescale_factor
         snake_case											=							do_normalize
         snake_case											=							image_mean
         snake_case											=							image_std
         snake_case											=							do_pad
    def 						a_     (			self       ):
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_pad": self.do_pad,
         }




    def 						a_     (			self     ,   __snake_case     ,   __snake_case=False       ):
         if not batched:
              snake_case											=							image_inputs[0]
              if isinstance(__snake_case     ,   Image.Image       ):
                   snake_case					,    snake_case											=							image.size
              else:
                   snake_case					,    snake_case											=							image.shape[1], image.shape[2]
              if w < h:
                   snake_case											=							int(self.size['''shortest_edge'''] * h / w       )
                   snake_case											=							self.size['''shortest_edge''']
              elif w > h:
                   snake_case											=							self.size['''shortest_edge''']
                   snake_case											=							int(self.size['''shortest_edge'''] * w / h       )
              else:
                   snake_case											=							self.size['''shortest_edge''']
                   snake_case											=							self.size['''shortest_edge''']

         else:
              snake_case											=							[]
              for image in image_inputs:
                   snake_case					,    snake_case											=							self.get_expected_values([image]       )
                   expected_values.append((expected_height, expected_width)       )
              snake_case											=							max(__snake_case     ,   key=lambda __snake_case       : item[0]       )[0]
              snake_case											=							max(__snake_case     ,   key=lambda __snake_case       : item[1]       )[1]

         return expected_height, expected_width






@require_torch
@require_vision
class     A__   (       snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						DetrImageProcessor if is_vision_available() else None
    def 						a_     (			self       ):
         snake_case											=							DetrImageProcessingTester(self       )
    @property
    def 						a_     (			self       ):
         return self.image_processor_tester.prepare_image_processor_dict()
    def 						a_     (			self       ):
         snake_case											=							self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(__snake_case     ,   '''image_mean'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''image_std'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''do_normalize'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''do_rescale'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''rescale_factor'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''do_resize'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''size'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''do_pad'''       )       )
    def 						a_     (			self       ):
         snake_case											=							self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size     ,   {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}       )
         self.assertEqual(image_processor.do_pad     ,   __snake_case       )

         snake_case											=							self.image_processing_class.from_dict(
             self.image_processor_dict     ,   size=4_2     ,   max_size=8_4     ,   pad_and_return_pixel_mask=__snake_case       )
         self.assertEqual(image_processor.size     ,   {'''shortest_edge''': 4_2, '''longest_edge''': 8_4}       )
         self.assertEqual(image_processor.do_pad     ,   __snake_case       )
    def 						a_     (			self       ):
         pass
    def 						a_     (			self       ):
         # Initialize image_processing
         snake_case											=							self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         snake_case											=							prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__snake_case       )
         for image in image_inputs:
              self.assertIsInstance(__snake_case     ,   Image.Image       )

         # Test not batched input
         snake_case											=							image_processing(image_inputs[0]     ,   return_tensors='''pt'''       ).pixel_values

         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case       )

         self.assertEqual(
             encoded_images.shape     ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,   )

         # Test batched
         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case     ,   batched=__snake_case       )

         snake_case											=							image_processing(__snake_case     ,   return_tensors='''pt'''       ).pixel_values
         self.assertEqual(
             encoded_images.shape     ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )     ,   )
    def 						a_     (			self       ):
         # Initialize image_processing
         snake_case											=							self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         snake_case											=							prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__snake_case     ,   numpify=__snake_case       )
         for image in image_inputs:
              self.assertIsInstance(__snake_case     ,   np.ndarray       )

         # Test not batched input
         snake_case											=							image_processing(image_inputs[0]     ,   return_tensors='''pt'''       ).pixel_values

         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case       )

         self.assertEqual(
             encoded_images.shape     ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,   )

         # Test batched
         snake_case											=							image_processing(__snake_case     ,   return_tensors='''pt'''       ).pixel_values

         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case     ,   batched=__snake_case       )

         self.assertEqual(
             encoded_images.shape     ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )     ,   )
    def 						a_     (			self       ):
         # Initialize image_processing
         snake_case											=							self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         snake_case											=							prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__snake_case     ,   torchify=__snake_case       )
         for image in image_inputs:
              self.assertIsInstance(__snake_case     ,   torch.Tensor       )

         # Test not batched input
         snake_case											=							image_processing(image_inputs[0]     ,   return_tensors='''pt'''       ).pixel_values

         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case       )

         self.assertEqual(
             encoded_images.shape     ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,   )

         # Test batched
         snake_case											=							image_processing(__snake_case     ,   return_tensors='''pt'''       ).pixel_values

         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case     ,   batched=__snake_case       )

         self.assertEqual(
             encoded_images.shape     ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )     ,   )
    @slow
    def 						a_     (			self       ):
         # prepare image and target
         snake_case											=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
         with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''     ,   '''r'''       ) as f:
              snake_case											=							json.loads(f.read()       )

         snake_case											=							{'''image_id''': 3_9_7_6_9, '''annotations''': target}

         # encode them
         snake_case											=							DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''       )
         snake_case											=							image_processing(images=__snake_case     ,   annotations=__snake_case     ,   return_tensors='''pt'''       )

         # verify pixel values
         snake_case											=							torch.Size([1, 3, 8_0_0, 1_0_6_6]       )
         self.assertEqual(encoding['''pixel_values'''].shape     ,   __snake_case       )

         snake_case											=							torch.tensor([0.2796, 0.3138, 0.3481]       )
         self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]     ,   __snake_case     ,   atol=1E-4       )       )

         # verify area
         snake_case											=							torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']     ,   __snake_case       )       )
         # verify boxes
         snake_case											=							torch.Size([6, 4]       )
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape     ,   __snake_case       )
         snake_case											=							torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]     ,   __snake_case     ,   atol=1E-3       )       )
         # verify image_id
         snake_case											=							torch.tensor([3_9_7_6_9]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']     ,   __snake_case       )       )
         # verify is_crowd
         snake_case											=							torch.tensor([0, 0, 0, 0, 0, 0]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']     ,   __snake_case       )       )
         # verify class_labels
         snake_case											=							torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']     ,   __snake_case       )       )
         # verify orig_size
         snake_case											=							torch.tensor([4_8_0, 6_4_0]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']     ,   __snake_case       )       )
         # verify size
         snake_case											=							torch.tensor([8_0_0, 1_0_6_6]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']     ,   __snake_case       )       )




    @slow
    def 						a_     (			self       ):
         # prepare image, target and masks_path
         snake_case											=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
         with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''     ,   '''r'''       ) as f:
              snake_case											=							json.loads(f.read()       )

         snake_case											=							{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

         snake_case											=							pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''       )

         # encode them
         snake_case											=							DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''       )
         snake_case											=							image_processing(images=__snake_case     ,   annotations=__snake_case     ,   masks_path=__snake_case     ,   return_tensors='''pt'''       )

         # verify pixel values
         snake_case											=							torch.Size([1, 3, 8_0_0, 1_0_6_6]       )
         self.assertEqual(encoding['''pixel_values'''].shape     ,   __snake_case       )

         snake_case											=							torch.tensor([0.2796, 0.3138, 0.3481]       )
         self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]     ,   __snake_case     ,   atol=1E-4       )       )

         # verify area
         snake_case											=							torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']     ,   __snake_case       )       )
         # verify boxes
         snake_case											=							torch.Size([6, 4]       )
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape     ,   __snake_case       )
         snake_case											=							torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]     ,   __snake_case     ,   atol=1E-3       )       )
         # verify image_id
         snake_case											=							torch.tensor([3_9_7_6_9]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']     ,   __snake_case       )       )
         # verify is_crowd
         snake_case											=							torch.tensor([0, 0, 0, 0, 0, 0]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']     ,   __snake_case       )       )
         # verify class_labels
         snake_case											=							torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']     ,   __snake_case       )       )
         # verify masks
         snake_case											=							8_2_2_8_7_3
         self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()     ,   __snake_case       )
         # verify orig_size
         snake_case											=							torch.tensor([4_8_0, 6_4_0]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']     ,   __snake_case       )       )
         # verify size
         snake_case											=							torch.tensor([8_0_0, 1_0_6_6]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']     ,   __snake_case       )       )




